A Guide to Design and Analysis of Algorithms


Soubhik Chakraborty, PhD – Professor, Department of Mathematics, Birla Institute of Technology, Mesra, Jharkhand, India
Prashant Pranav, PhD – Assistant Professor, Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand, India
Naghma Khatoon – Assistant Professor, Faculty of Computing and Information Technology, Usha Martin University, Jharkhand, India
Sandip Dutta, PhD – Professor, Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand, India

Series: Computer Science, Technology and Applications
BISAC: COM014000
DOI: 10.52305/HVZD7283

As there can be more than one algorithm for the same problem, designing and analyzing an algorithm becomes important in order to make it as efficient and robust as possible. This book will serve as a guide to design and analysis of computer algorithms. Chapter One provides an overview of different algorithm design techniques and the various applications of such techniques. Chapter Two reviews the divide and conquer strategy and the algorithm types that employ it. Chapter Three explores greedy algorithms and some problems that can be solved with this approach. Chapter Four discusses in depth the dynamic programming approach. Chapter Five provides a solution to the N-Queens problem utilizing a backtracking approach. Chapter Six elucidates the reader to branch and bound techniques and provides three solutions to problems implementing them. Part II of this book begins with Chapter Seven, where two different approaches to the analysis of algorithms are discussed. Chapter Eight reviews randomized algorithms through an empirical lens. Chapter Nine discusses Master Theorem and the many kinds of problems this Theorem can solve. Chapter Ten, the final chapter, provides notes on the empirical complexity analysis of algorithms.

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Table of Contents


Chapter 1. Introduction to the Design of Algorithms

Chapter 2. Divide and Conquer

Chapter 3. Greedy Algorithms

Chapter 4. Dynamic Programming

Chapter 5. Backtracking

Chapter 6. Branch and Bound

Chapter 7. Introduction to the Analysis of Algorithms

Chapter 8. Randomized Algorithms

Chapter 9. Master Theorem

Chapter 10. A Note on Empirical Complexity Analysis

About the Authors



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